A Multiscale Approach Indicates a Severe Reduction in Atlantic Forest Wetlands and Highlights that S?o Paulo Marsh Antwren Is on the Brink of Extinction

Over the last 200 years the wetlands of the Upper Tietê and Upper Paraíba do Sul basins, in the southeastern Atlantic Forest, Brazil, have been almost-completely transformed by urbanization, agriculture and mining. Endemic to these river basins, the São Paulo Marsh Antwren (Formicivora paludicola) survived these impacts, but remained unknown to science until its discovery in 2005. Its population status was cause for immediate concern. In order to understand the factors imperiling the species, and provide guidelines for its conservation, we investigated both the species’ distribution and the distribution of areas of suitable habitat using a multiscale approach encompassing species distribution modeling, fieldwork surveys and occupancy models. Of six species distribution models methods used (Generalized Linear Models, Generalized Additive Models, Multivariate Adaptive Regression Splines, Classification Tree Analysis, Artificial Neural Networks and Random Forest), Random Forest showed the best fit and was utilized to guide field validation. After surveying 59 sites, our results indicated that Formicivora paludicola occurred in only 13 sites, having narrow habitat specificity, and restricted habitat availability. Additionally, historic maps, distribution models and satellite imagery showed that human occupation has resulted in a loss of more than 346 km2 of suitable habitat for this species since the early twentieth century, so that it now only occupies a severely fragmented area (area of occupancy) of 1.42 km2, and it should be considered Critically Endangered according to IUCN criteria. Furthermore, averaged occupancy models showed that marshes with lower cattail (Typha dominguensis) densities have higher probabilities of being occupied. Thus, these areas should be prioritized in future conservation efforts to protect the species, and to restore a portion of Atlantic Forest wetlands, in times of unprecedented regional water supply problems.     

普洱季风常绿阔叶林乔木物种多样性高分辨率遥感估测

季风常绿阔叶林是我国南亚热带典型的地带性植被,也是云南省普洱地区重要森林类型。季风常绿阔叶林乔木物种多样性遥感估测对研究区域尺度生物多样性格局及其规律具有重要作用。根据光谱异质性假说和环境异质性假说,首先使用1m空间分辨率的机载高光谱数据和激光雷达数据提取了光谱多样性特征和垂直结构特征。然后利用基于随机森林算法的递归特征消除方法选择对研究区森林乔木物种多样性指数具有较好解释能力的遥感特征,并对Shannon-Winner物种多样性指数进行建模、制图。研究结果表明：(1)基于机载LiDAR数据提取的垂直结构特征和机载高光谱数据提取的光谱多样性特征均对研究区森林乔木物种多样性具有较好的解释能力,随机森林模型估测结果分别为R²=0.48,RMSE=0.46和R²=0.5,RMSE=0.45;两种数据源融合可以进一步提高遥感数据的森林乔木物种多样性估测精度,随机森林估测模型R²和RMSE分别为0.69和0.37。(2)机载激光雷达数据对研究区针阔混交林乔木物种多样性的估测能力优于机载高光谱数据。(3)机器学习方法有助于从高维遥感...     

Forest structure predicts species richness and functional diversity in Amazonian mixed-species bird flocks

Secondary forest has the potential to act as an important habitat for biodiversity and restoring ecological benefits. Functional diversity, which includes morphological and behavioral traits that mediate species interactions with the surrounding environment, relates to the resilience of ecosystems. To assess the relationship between habitat structural differences in primary and secondary forest and the resultant differences in functional diversity of avian species, we followed 11 mixed-species flocks at the Biological Dynamics of Forest Fragments Project, near Manaus, Brazil. We used remote sensing LiDAR to assess which three-dimensional forest structural features are most closely associated with variation in species richness and functional diversity in secondary and primary tropical forest flocks. The species richness of flocks in primary forest increased in areas with higher elevation and higher leaf area density in the understory and subcanopy but was not correlated with habitat structure in secondary forest. Functional diversity increased at lower elevations and with a denser subcanopy in both primary forest and secondary forest but only increased with greater understory leaf area density in primary forest. Together, these results indicate that a dense subcanopy and understory can be important for mixed-species flocks and that flock richness and functional diversity can be predicted by vegetation structure.     

Forest species diversity mapping based on clustering algorithm

该研究基于机载激光雷达(LiDAR)和高光谱数据, 从森林物种叶片的生理化学源头探寻生化特征与光谱特征的内在关联, 探讨生化多样性、光谱多样性与物种多样性之间的响应机制, 选择最优植被指数并结合最优结构参数, 通过聚类方法构建森林物种多样性遥感估算模型, 在古田山自然保护区开展森林乔木物种多样性监测。研究结果表明: (1)从16种叶片生化组分中, 筛选出叶绿素a、叶绿素b、类胡萝卜素、叶片含水量、比叶面积、纤维素、木质素、氮、磷和碳可通过偏最小二乘法用叶片光谱有效模拟(R² = 0.60-0.79, p < 0.01), 并选择有效的植被指数: 转换型吸收反射指数/优化型土壤调整指数(TCARI/OSAVI)、类胡萝卜素反射指数(CRI)、水波段指数(WBI)、比值植被指数(RVI)、生理反射指数(PRI)和冠层叶绿素浓度指数(CCCI)表征相应的最优生化组分; (2)基于机载LiDAR数据利用结合形态学冠层控制的分水岭算法获得高精度单木分离结果(R ² = 0.77, RMSE = 16.48), 同时采用逐步回归方法从常用的森林结构参数中选取树高和偏度作为最优结构参数(R ² = 0.32, p < 0.01); (3)基于6个最优植被指数和2个最优结构参数, 以20 m × 20 m为窗口通过自适应模糊C均值方法进行聚类, 实现了研究区森林乔木物种丰富度(Richness, R ²= 0.56, RMSE = 1.81)和多样性指数Shannon-Wiener (R ² = 0.83, RMSE = 0.22)与Simpson (R ² = 0.85, RMSE = 0.09)的成图。该研究在冠层尺度上获取了与物种多样性相关的生化、光谱和结构参数, 将单木个体作为最小单元, 利用聚类算法直接估算物种类别差异, 无需判定具体的树种属性, 是利用遥感数据进行区域尺度森林物种多样性监测与成图的实践, 可为亚热带地区常绿阔叶林的物种多样性监测提供借鉴。     

A Tale of Two “Forests”: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping

Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including—in the latter case—x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called “out-of-bag”), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha⁻¹ when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.     

利用聚类算法监测森林乔木物种多样性

该研究基于机载激光雷达(LiDAR)和高光谱数据, 从森林物种叶片的生理化学源头探寻生化特征与光谱特征的内在关联, 探讨生化多样性、光谱多样性与物种多样性之间的响应机制, 选择最优植被指数并结合最优结构参数, 通过聚类方法构建森林物种多样性遥感估算模型, 在古田山自然保护区开展森林乔木物种多样性监测。研究结果表明: (1)从16种叶片生化组分中, 筛选出叶绿素a、叶绿素b、类胡萝卜素、叶片含水量、比叶面积、纤维素、木质素、氮、磷和碳可通过偏最小二乘法用叶片光谱有效模拟(R² = 0.60-0.79, p < 0.01), 并选择有效的植被指数: 转换型吸收反射指数/优化型土壤调整指数(TCARI/OSAVI)、类胡萝卜素反射指数(CRI)、水波段指数(WBI)、比值植被指数(RVI)、生理反射指数(PRI)和冠层叶绿素浓度指数(CCCI)表征相应的最优生化组分; (2)基于机载LiDAR数据利用结合形态学冠层控制的分水岭算法获得高精度单木分离结果(R ² = 0.77, RMSE = 16.48), 同时采用逐步回归方法从常用的森林结构参数中选取树高和偏度作为最优结构参数(R ² = 0.32, p < 0.01); (3)基于6个最优植被指数和2个最优结构参数, 以20 m × 20 m为窗口通过自适应模糊C均值方法进行聚类, 实现了研究区森林乔木物种丰富度(Richness, R ²= 0.56, RMSE = 1.81)和多样性指数Shannon-Wiener (R ² = 0.83, RMSE = 0.22)与Simpson (R ² = 0.85, RMSE = 0.09)的成图。该研究在冠层尺度上获取了与物种多样性相关的生化、光谱和结构参数, 将单木个体作为最小单元, 利用聚类算法直接估算物种类别差异, 无需判定具体的树种属性, 是利用遥感数据进行区域尺度森林物种多样性监测与成图的实践, 可为亚热带地区常绿阔叶林的物种多样性监测提供借鉴。     

Habitat specificity and diversity of tree species in an African wet tropical forest

Niche differentiation with respect to habitat has been hypothesized to shape patterns of diversity and species distributions in plant communities. African forests have been reported to be relatively less diverse compared to highly diversed regions of the Amazonian or Southeast Asian forests, and might be expected to have less niche differentiation. We examined patterns of structural and floristic differences among five topographically defined habitats for 494 species with stems ≥1 cm dbh in a 50-ha plot in Korup National Park, Cameroon. In addition, we tested for species–habitat associations for 272 species (with more than 50 individuals in the plot) using Torus translation randomization tests. Tree density and basal area were lowest in areas with negative convexity, which contained streams or were inundated during rainy periods and highest in moist well-drained habitats. Species composition and diversity varied along the topographical gradient from low flat to ridge top habitats. The low depression and low flat habitats were characterized by high diversity and similar species composition, relative to slopes, high gullies and ridge tops. Sixty-three percent of the species evaluated showed significant positive associations with at least one of the five habitat types. The majority of associations were with low depressions (75 species) and the fewest with ridge tops (8 species). The large number of species–habitat associations and the pronounced contrast between low (valley) and elevated (ridgetop) habitats in the Korup plot shows that niche differentiation with respect to edaphic variables (e.g., soil moisture, nutrients) contributes to local scale tree species distributions and to the maintenance of diversity in African forests.     

When species accumulation curves intersect: implications for ranking diversity using small samples

Diversity in biological communities frequently is compared using species accumulation curves, plotting observed species richness versus sample size. When species accumulation curves intersect, the ranking of communities by observed species richness depends on sample size, creating inconsistency in comparisons of diversity. We show that species accumulation curves for two communities are expected to intersect when the community with lower actual species richness has higher Simpson diversity (probability that two random individuals belong to different species). This may often occur when comparing communities that differ in habitat heterogeneity or disturbance, as we illustrate using data from neotropical butterflies. In contrast to observed species richness, estimated Simpson diversity always produces a consistent expected ranking among communities across sample sizes, with the statistical accuracy to confidently rank communities using small samples. Simpson diversity should therefore be particularly useful in rapid assessments to prioritize areas for conservation.     

Scale-dependent spatial patterns of species diversity in the tropical montane rain forest in Jianfengling,Hainan Island,China

Aims Spatial distribution patterns and formation mechanisms of species diversity are fundamental issues in community ecology. The objectives of this study are to assess the species diversity patterns at the different spatial scales in Jianfengling, Hainan Island, China.
Methods Based on the dataset from the 60 hm² plot in the tropical montane rain forest in Jianfengling, Hainan Island, the spatial distribution patterns of species richness, species abundance, Shannon-Wiener, Simpson and Pielou’s evenness indices were analyzed at six spatial scales, including 5 m × 5 m, 10 m × 10 m, 20 m × 20 m, 40 m × 40 m, 100 m × 100 m, and 200 m × 200 m, respectively.Important findings
Results showed that spatial distribution patterns of species richness, species abundance and Shannon-Wiener index were much more obviously changed with the spatial scales than Simpson and Pielou’s evenness indices. Change of variance of the species richness with the increase of spatial scales was unimodal, which had the maximum value at the 20 m × 20 m scale. Variance of the species abundance showed a linear relationship with the increase of spatial scales. The positive relationship between species richness and abundance gradually decreased and even disappeared with the increase of sampling scales, which may be correlated with the increase of habitat heterogeneity. The effects of spatial scales on Shannon-Wiener, Simpson, and Pielou’s evenness indices may be also correlated with the composition of rare species in the plot.     

Using Satellite and Airborne LiDAR to Model Woodpecker Habitat Occupancy at the Landscape Scale

Incorporating vertical vegetation structure into models of animal distributions can improve understanding of the patterns and processes governing habitat selection. LiDAR can provide such structural information, but these data are typically collected via aircraft and thus are limited in spatial extent. Our objective was to explore the utility of satellite-based LiDAR data from the Geoscience Laser Altimeter System (GLAS) relative to airborne-based LiDAR to model the north Idaho breeding distribution of a forest-dependent ecosystem engineer, the Red-naped sapsucker (Sphyrapicus nuchalis). GLAS data occurred within ca. 64 m diameter ellipses spaced a minimum of 172 m apart, and all occupancy analyses were confined to this grain scale. Using a hierarchical approach, we modeled Red-naped sapsucker occupancy as a function of LiDAR metrics derived from both platforms. Occupancy models based on satellite data were weak, possibly because the data within the GLAS ellipse did not fully represent habitat characteristics important for this species. The most important structural variables influencing Red-naped Sapsucker breeding site selection based on airborne LiDAR data included foliage height diversity, the distance between major strata in the canopy vertical profile, and the vegetation density near the ground. These characteristics are consistent with the diversity of foraging activities exhibited by this species. To our knowledge, this study represents the first to examine the utility of satellite-based LiDAR to model animal distributions. The large area of each GLAS ellipse and the non-contiguous nature of GLAS data may pose significant challenges for wildlife distribution modeling; nevertheless these data can provide useful information on ecosystem vertical structure, particularly in areas of gentle terrain. Additional work is thus warranted to utilize LiDAR datasets collected from both airborne and past and future satellite platforms (e.g. GLAS, and the planned IceSAT2 mission) with the goal of improving wildlife modeling for more locations across the globe.     

海南尖峰岭热带山地雨林物种多样性空间分布格局的尺度效应

物种多样性的空间分布格局和维持机制是群落生态学的基本问题。为了探讨海南尖峰岭地区物种多样性空间分布格局的尺度效应, 以海南尖峰岭热带山地雨林60 hm²样地为研究对象, 分析了物种丰富度、物种多度、Shannon-Wiener指数、Simpson指数以及Pielou均匀度指数随6个空间取样尺度(5 m × 5 m、10 m × 10 m、20 m × 20 m、40 m × 40 m、100 m ×100 m、200 m × 200 m)的变化。结果表明: 相比Simpson指数和Pielou均匀度指数, 物种丰富度、多度以及Shannon-Wiener指数具有更为明显的空间尺度效应; 物种丰富度的方差随取样尺度增加呈现单峰分布特征, 并且在20 m × 20 m尺度上达到最大值, 而物种多度的方差随取样尺度的增加而增大; 物种丰富度和多度的正相关性随着取样尺度的增加逐渐减小甚至消失, 这可能与随取样尺度增加生境异质性增加有关; 取样尺度对3个物种多样性指数空间分布的影响可能与研究区域内稀有种的组成有关。     

Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes

The loss of species is an ongoing process threatening the services provided by ecosystems for humanity. In agricultural areas, which globally occupy the largest areas, species diversity is strongly dependent on the farmers' management. If management allows for a landscape structure with many remnants of natural or semi-natural vegetation, the diversity and amount of flora and fauna is higher compared to intensive agricultural landscapes with large, continuous field parcels and a low crop variety. Knowledge of agricultural management is crucial for the development and monitoring of political strategies that aim to enhance or conserve species in agricultural areas. Remote sensing is a cost effective way to acquire this knowledge for large spatial areas with high temporal resolution. Since 2008, modern satellite-based radar sensors deliver images of unprecedented high quality. Since the acquisition of radar images is not restricted by atmospheric conditions, it is very capable of multitemporal classifications. In the presented study, possibilities for supervised multitemporal classification of non crop areas are investigated based on TerraSAR-X images and two different classifiers (Maximum Likelihood and Random Forest). The best results were achieved for the classification of woody structures where producer's accuracies are above 80%. Despite lower values for the other classes (flower strips 75%, grasslands 75.8% and herbaceous 57%), these classes are easily recognized. This is illustrated by different map examples. The presented results can contribute essentially to the monitoring, investigation and increasing of habitat structures in agricultural areas.     

Habitat and conservation of the enigmatic damselfly <Emphasis Type="Italic">Ischnura pumilio</Emphasis>

Ischnura pumilio is threatened in the UK and its habitat requirements are not well understood. This study tests previously held notions of the habitat requirements of I. pumilio, investigates the features of a habitat influencing odonate species composition and provides recommendations for habitat creation and management for I. pumilio persistence. Thirty-one sites across south west England with past I. pumilio records were surveyed in 2006. Environmental variables and odonate abundance were recorded. Odonate species composition and I. pumilio abundance were related to environmental variables using multivariate techniques and GLM. Ischnura pumilio was found at a wide variety of habitat types; key habitat features were a muddy substrate with some open ground, turbid water, and low levels of shade. It was associated with increased structural diversity of vegetation away from water but low maximum height; characteristic of early-successional sites. The variables predicting odonate composition were location, shade, level of disturbance, water depth, and cover of terrestrial dwarf shrubs and Sphagnum species. Vegetation height and structure were also highly influential to at least 20 m from water. This study indicates that odonate habitat management should include adjacent hinterland. Management for I. pumilio may be complicated by the species’ use of two habitat types, each with associated problems. Furthermore, odonate species diversity was negatively associated with I. pumilio abundance, which may cause conflict of interest when managing habitats.     

LiDAR as a rapid tool to predict forest habitat types in Natura 2000 networks

Management strategies for the conservation of biodiversity can be developed only with precise information on the spatial distribution of organisms on relevant, mostly regional, spatial scales. Current surrogates for approximating the distribution of biodiversity are habitats mapped within a number of national and international frameworks (e.g., Natura 2000), even though conventional habitat mapping is time consuming and requires well-trained personnel. Here we evaluated the use of light detection and ranging (LiDAR) to map forest habitat types to simplify the process. We used available data of habitat types for the Bavarian Forest National Park as a basis to predict habitat types with LiDAR-derived variables. Furthermore, we compared these results with predictions based on extensive ground-based climate, soil and vegetation data. Using linear and flexible discriminant analyses, we found that LiDAR is able to predict forest habitat types with the same overall accuracy as the extensive ground data for climate, soil and vegetation composition. Subtle differences in the vegetation structure between habitat types, particularly in the vertical and horizontal vegetation profiles, were captured by LiDAR. These differences in the physiognomy were in part caused by changes in altitude, which also influence tree species composition. We propose that the most-efficient way to identify forest habitat types according Natura 2000 is to combine remote-sensing LiDAR data with well-directed field surveys.     

桂林岩溶石山阴香群落的数量分类及其物种多样性研究

采用双向指示种分析(TWINSPAN),对桂林岩溶石山阴香群落进行数量分类,并应用Patrick丰富度指数、Simpson指数、Shannon-Wiener多样性指数以及Pielou均匀度指数比较分析了各群丛类型的物种多样性特征。结果表明:(1)阴香群落可划分为5种群丛类型,分别为:Ⅰ.阴香-山合欢-荩草群丛;Ⅱ.阴香-石山桂花+粗糠柴-麦冬群丛;Ⅲ.阴香-石山桂花-麦冬+凸脉苔草群丛;Ⅳ.阴香-石山桂花+小叶女贞-麦冬群丛;Ⅴ.阴香-香槐+石山桂花-剑叶凤尾蕨+普通假毛蕨群丛;(2)阴香群落的结构较为简单,物种多样性相对较低;在5个群丛类型中,群丛Ⅲ和群丛Ⅴ的物种丰富度和多样性指数较高,群丛Ⅱ的多样性指数较低;由于生境条件和人为干扰程度的不同,群丛类型的物种多样性存在一定差异。     

Enhanced migratory waterfowl distribution modeling by inclusion of depth to water table data

In addition to being used as a tool for ecological understanding, management and conservation of migratory waterfowl rely heavily on distribution models; yet these models have poor accuracy when compared to models of other bird groups. The goal of this study is to offer methods to enhance our ability to accurately model the spatial distributions of six migratory waterfowl species. This goal is accomplished by creating models based on species-specific annual cycles and introducing a depth to water table (DWT) data set. The DWT data set, a wetland proxy, is a simulated long-term measure of the point either at or below the surface where climate and geological/topographic water fluxes balance. For species occurrences, the USGS' banding bird data for six relatively common species was used. Distribution models are constructed using Random Forest and MaxEnt. Random Forest classification of habitat and non-habitat provided a measure of DWT variable importance, which indicated that DWT is as important, and often more important, to model accuracy as temperature, precipitation, elevation, and an alternative wetland measure. MaxEnt models that included DWT in addition to traditional predictor variables had a considerable increase in classification accuracy. Also, MaxEnt models created with DWT often had higher accuracy when compared with models created with an alternative measure of wetland habitat. By comparing maps of predicted probability of occurrence and response curves, it is possible to explore how different species respond to water table depth and how a species responds in different seasons. The results of this analysis also illustrate that, as expected, all waterfowl species are tightly affiliated with shallow water table habitat. However, this study illustrates that the intensity of affiliation is not constant between seasons for a species, nor is it consistent between species.     

激光雷达技术在动物生态学领域的研究进展

激光雷达(light detection and ranging, LiDAR)作为一门新兴的主动遥感技术, 近年来由于在提取和反演森林参数水平上不断提高, 被越来越多地应用于动物生态学研究中。本文通过整理和搜集国内外文献, 对激光雷达的技术特点及其在森林参数提取和动物生境上的研究进展进行综述, 指出当前基于LiDAR的森林参数反演算法主要服务于森林资源调查或林学研究, 缺少对动物生态或生理意义相关的参数量化信息。目前该技术在国内的动物生态学方面的应用较少, 尚未见文章发表。通过总结国外学者的研究, 分别从动物生境选择与三维森林结构的关系、栖息地立体生境制图、生物多样性评估和物种分布模型预测三个方面综述了LiDAR在动物生态学研究中的应用现状。相比传统方法, LiDAR技术提供的高精度三维结构信息, 能够显著提高动物生境质量的评估、生物多样性的监测水平和物种分布模型的评价精度, 有利于从机理上加深对物种生境选择和集群过程的理解。但目前LiDAR技术的应用主要集中在对已知的生态关系研究, 尤其是冠层结构与动物分布的关系, 缺少对林下层生活的动物生境质量和生物多样性的监测和评估, 同时很多有关动物生存和繁衍与立体生境的关系研究有待从LiDAR数据中进一步挖掘分析。未来应加强对森林林下层三维信息的提取, 提高林下层动物生境质量和生物多样性的监测水平, 同时建立适用于动物生态和生理意义相关的参数, 为动物生境质量和生物多样性的评估提供标准的量化指标。     

缙云山生境片断化对常绿阔叶林生物多样性的影响

采用Simpson、Shannon-Wiener和Hill多样性指数以及逐步多元回归分析方法,在缙云山进行了植物群落调查、小气候观测和土壤肥力的测定以及各变量之间关系的分析。结果表明:片断常绿阔叶林物种多样性指数比连续常绿阔叶林低,不同生活型的表现不同;各片断阔叶林斑块边缘均存在明显的小气候边缘效应。随着斑块面积的减小,各斑块的ΔTa、林缘及林内ΔTs、林缘和林内ΔRH、林缘和林内Vmax、林缘和林内ΔPAR(正午)均呈增大的趋势。各斑块的小气候边缘效应,以最大斑块波及林内的深度最浅(约至林内15m)、最小斑块波及林内的深度最深(约至林内25m);常绿阔叶林物种多样性受林内外有效光合辐射(正午)差、气温日较差、土壤含水量、土壤有机质含量和土壤有效钾等因素的影响。     

Effects of Climate Change on Habitat Availability and Configuration for an Endemic Coastal Alpine Bird

North America’s coastal mountains are particularly vulnerable to climate change, yet harbour a number of endemic species. With little room “at the top” to track shifting climate envelopes, alpine species may be especially negatively affected by climate-induced habitat fragmentation. We ask how climate change will affect the total amount, mean patch size, and number of patches of suitable habitat for Vancouver Island White-tailed Ptarmigan (Lagopus leucura saxatilis; VIWTP), a threatened, endemic alpine bird. Using a Random Forest model and a unique dataset consisting of citizen science observations combined with field surveys, we predict the distribution and configuration of potential suitable summer habitat for VIWTP under baseline and future (2020s, 2050s, and 2080s) climates using three general circulation models and two greenhouse gas scenarios. VIWTP summer habitat is predicted to decline by an average of 25%, 44%, and 56% by the 2020s, 2050s, and 2080s, respectively, under the low greenhouse gas scenario and 27%, 59%, and 74% under the high scenario. Habitat patches are predicted to become fragmented, with a 52–79% reduction in mean patch size. The average elevation of suitable habitat patches is expected to increase, reflecting a loss of patches at lower elevations. Thus ptarmigan are in danger of being “squeezed off the mountain”, as their remaining suitable habitat will be increasingly confined to mountaintops in the center of the island. The extent to which ptarmigan will be able to persist in increasingly fragmented habitat is unclear. Much will depend on their ability to move throughout a more heterogeneous landscape, utilize smaller breeding areas, and survive increasingly variable climate extremes. Our results emphasize the importance of continued monitoring and protection for high elevation specialist species, and suggest that White-tailed Ptarmigan should be considered an indicator species for alpine ecosystems in the face of climate change.     

Habitat suitability of Gymnocladus assamicus - A critically endangered plant of Arunachal Pradesh,India using machine learning and statistical modeling

The present study sought to identify the potential distribution range of critically endangered Gymnocladus assamicus in Arunachal Pradesh based on published data and field collection. We used the Maxent model to estimate the range of distribution and the result was then compared with three other models, i.e., the Generalized Linear Model (GLM), the Bioclim and the Random Forest model to assess the species' habitat suitability. A total of 23 different environmental variables were used, including bioclimatic ones, monthly minimum and maximum temperature, monthly precipitation and elevation data. The Maxent output listed 12 variables explaining 99.9% variation in the model. In comparison, Maxent showed the maximum region under habitat suitability criteria (1884.48 km²), followed by Random Forest (70.73 km²) and Bioclim (11.62 km²) model. Except for the Maxent model, suitable habitats predicted by other models are highly restricted within and across the study species' current distribution range. The average model prediction shows an expanded distribution range for the species up to Tawang which is the closest district of currently known distribution of the species in the state. Thus, the present study recognizes the importance of the geographic range of G. assamicus, a critically endangered species with very limited spatial distribution range and also provides some specific details to explore possible habitats for the species in new areas of potential occurrence in Arunachal Pradesh, India.